System, method, and recording medium for efficient cohesive subgraph identifiation in entity collections for inlier and outlier detection

ABSTRACT

A similarity detection system receiving a plurality of input entities, the system including a cohesive subgraph identification device configured to calculate, based on attributes of the plurality of input entities, a first parameter and a second parameter based on the first parameter, and further configured to identify a plurality of subgraphs from the second parameter and a subgraph correlation tracking and clustering device configured to determine a relationship between different subgraphs based on a similarity factor between the second parameter and the plurality of subgraphs.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation application of U.S. patentapplication Ser. No. 14/853,028, filed on Sep. 14, 2015, the entirecontents of which are hereby incorporated by reference.

DETECTION

This invention was made with Government support under Contract No.:H98230-11-C-0276 awarded by the Department of Defense (DoD). TheGovernment has certain rights in this invention.

BACKGROUND

The present invention relates generally to image processing, and moreparticularly, but not by way of limitation, to a system, a method, and arecording medium including inputting entities, outlier objects can bedetected via efficient cohesive subgraph identification, and outputtingtwo lists; outlier entities and inlier entities.

In conventional media collection containing facial imagery, often timesthere is only a small portion of the collection that is relevant to aperson of interest. The rest of the collection is of zero value but addsa significant burden on the user or an analyst to be able to remove themfrom the collection.

Conventionally, outlier detection has been performed via geometric-basedmethods, such as PCA, Kernel PCA, Robust PCA, or Robust Kernel PCA.

Other conventional methods have performed outlier detection byprobabilistic modeling method, such as, kernel density estimator (KDE)and robust kernel density estimator (RKDE).

Another conventional outlier detection method has proposed performingthe outlier detection by a learning method such as one-class supportvector machines (OC-SVMs) or support vector data description (SVDD).

However, each conventional outlier detection method above, and all otherconventional outlier detection methods are limited in their applicationin that they have a high computational cost on a large dataset, requireprior knowledge, and difficult to be extended to an online case.

SUMMARY

In view of the foregoing and other problems, disadvantages, anddrawbacks of the aforementioned background art, it is desirable toprovide an improved way to perform similarity detection between inputentities and automatically eliminate outliers/inliers scattering amongpractical data collections.

An exemplary aspect of the disclosed invention provides a system,method, and non-transitory recording medium for detecting similaritiesbetween entities.

In an exemplary embodiment, the present invention can provide asimilarity detection system receiving a plurality of input entities, thesystem including a cohesive subgraph identification device configured tocalculate, based on attributes of the plurality of input entities, afirst parameter and a second parameter from the first parameter, andfurther configured to identify a plurality of subgraphs from the secondparameter and a subgraph correlation tracking and clustering deviceconfigured to determine a relationship between different subgraphs basedon a similarity factor between the second parameter and the plurality ofsubgraphs.

Further, in another exemplary embodiment, the present invention canprovide a similarity detection method, including receiving a pluralityof input entities, calculating, based on attributes of the plurality ofinput entities, a first parameter and a second parameter from the firstparameter, identifying a plurality of subgraphs from the secondparameter, and determining a relationship between different subgraphsbased on a similarity factor between the second parameter and theplurality of subgraphs.

Even further, in another exemplary embodiment, the present invention canprovide a non-transitory computer-readable recording medium recording asimilarity detection program, the program causing a computer to performreceiving a plurality of input entities, calculating, based onattributes of the plurality of input entities, a first parameter and asecond parameter from the first parameter, identifying a plurality ofsubgraphs from the second parameter, and determining a relationshipbetween different subgraphs based on a similarity factor between thesecond parameter and the plurality of subgraphs.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a block diagram illustrating a configuration ofa similarity detection system 101.

FIG. 2 exemplary shows a flowchart for a method for detectingsimilarities between entities.

FIG. 3 exemplarily shows an example of detected outliers.

FIG. 4 exemplary shows an example of detected inliers.

FIG. 5a exemplary shows a k 3-Core and a (k,d) (3,1)-Core.

FIG. 5b exemplary shows the propagation and aggregation process from onesource to multiple targets.

FIG. 6a exemplary shows (k, d) cores generation by a “Zigzag” algorithmused by the cohesive subgraph identification device 104.

FIG. 6b exemplary shows (k, d) cores generation by a “NodeFirst”algorithm used by the cohesive subgraph identification device 104.

FIG. 6c exemplary shows an “Outlier Detection” algorithm used by thesubgraph correlation tracking and clustering device 105.

FIG. 7 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 8 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 9 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-9, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessary to scale. On the contrary, the dimensionsof the various features can be arbitrarily expanded or reduced forclarity. Exemplary embodiments are provided below for illustrationpurposes and do not limit the claims.

It should be noted that the exemplary embodiments described hereinrelate generally to facial images. However, the invention is not limitedto facial image detection and/or to similarities between facial images.That is, the invention can be related to any input entities to detectinlier and outliers within the entities based on similarities betweenthe entities which the attributes of the entities can be representedmathematically (i.e., as a matrix or vector). Further, the invention canbe broadly related to any list of input entities to detect inlier andoutliers within the list of input entities such as words or lists ofwords. Thus, the entities input into the similarity detection system 101do not need to be images, but can be any entities in which similarity isdesired to be detected. The exemplary embodiment of facial image inputis merely done for ease of explanation but, in no way limits theinvention.

With reference now to FIG. 1, the similarity detection system 101comprises a face detection and verification device 102, a multi-scalescale-invariant feature transform (SIFT) and spatial histograms device103, a cohesive subgraph identification device 104, and a subgraphcorrelation tracking and clustering device 105. The similarity detectionsystem 101 receives a plurality of input images (entities) from animaging device 130. The similarity detection system 101 includes aprocessor 180 and a memory 190, with the memory 190 storing instructionsto cause the processor 180 to execute each device of the similaritydetection system 101.

Although as shown in FIGS. 7-9 and as described later, the computersystem/server 12 is exemplarily shown in cloud computing node 10 as ageneral-purpose computing device which may execute in a layer thesimilarity detection system 101 (FIG. 9), it is noted that the presentinvention can be implemented outside of the cloud environment.

Given a list of input images containing multiple images, the similaritydetection system 101 can output two lists of face images as shown inFIGS. 3 and 4; outlier images and inlier images. That is, for the imagelist, the similarity detection system provides an efficient approach forautomatically removing outliers faces from noisy list images. The imagelist may include static images.

The imaging device 130 inputs a list of face images with differentpersons faces into the similarity detection system 101. The list of faceimages can be obtained as crawled images from a website, for example. Inanother exemplary embodiment, a web search can be performed for a personof interest (or object). The resulting images from the web search can becrawled into an image list and input into the similarity detectionsystem 101. Further, in another exemplary embodiment, the list of imagesmay be compiled manually.

The face detection and verification device 102 extracts a face regionfrom the original image of the images and extracts shift features fromthe face region. The face detection and verification device 102 excludesitems in an image that may look like a face, but are not a real face ofa person (e.g., a “smiley face” drawn on a wall).

In other words, the face detection and verification device 102identifies the region of an image that is in fact a face and verifiesthat that region is actually a face of a person. Further, the facedetection and verification device 102 verifies that only one face existsin the image. That is, the face detection and verification device 102identifies the number of face regions detected in the image and wouldremove images from the input list with multiple faces in the image. Thefiltered image list by face detection and verification device 102 willcontain images with only a single face. Face detection and verificationdevice 102 may verify faces (or objects) according to known methods.Also, the face detection and verification device 102 can verify that oneentity is in each of the entities which are input into the similaritydetection system. That is, the face detection and verification device102 can detect and verify entities.

The multi-scale SIFT and spatial histograms device 103 receives thefiltered list of images from the face detection and verification device102 and transforms the images into similar vectors such that a distancebetween points on the images can be detected to calculate a similaritybetween images. That is, the filtered list of images can contain imagesof different sizes, lighting, pose, etc. (i.e., 100 pixels by 100 pixelsfor one image and 150 pixels by 150 pixels for another image). Themulti-scale SIFT and spatial histograms device 103 detects fixedfeatures out of each image. Multi-scale SIFT is a known technique in thefield to detect fixed features from an input image.

The multi-scale SIFT and spatial histograms device 103 transforms eachinput image to output a same size vector such that analysis can be doneand distances between vector points can be determined in order tocalculate similarity between images. In other words, no matter what theinput size of the image to the multi-scale SIFT and spatial histogramsdevice 103, the multi-scale SIFT and spatial histograms device 103 willtransform all of the images into a same size vector (or matrix).Further, the multi-scale SIFT and spatial histograms device 103 matcheseach vector to each other between all of the images. The multi-scaleSIFT and spatial histograms device 103 outputs a fixed size featurevector (or matrix) to the cohesive subgraph identification device 104.For example, if the list of images input into the similarity detectionsystem 101 includes three images, the multi-scale SIFT and spatialhistograms device 103 will output three 4,096 diamond shell vectors(i.e., fixed size feature vectors) for each image.

The cohesive subgraph identification device 104 generates a subgraph forall the input images, where each vertex from the fixed size featurevector represents the face image, and edges represent the similaritybetween two face image features.

The cohesive subgraph identification device 104 generates k-cores fromthe fixed size feature vector, further generates (k, d) cores via aZigzag algorithm (algorithm 1 as depicted in FIG. 6a ) or NodeFirstalgorithm (algorithm 2 as depicted in FIG. 6b ), and identifiessubgraphs from the (k, d) core structure.

More specifically, the cohesive subgraph identification device 104generates k-cores from all nodes represented as g(V_(g),E_(g)) of thefixed size feature matrix. The k-Cores are generated in polynomial time.By adjusting k, the cohesive subgraph identification device 104generates k-Cores with desired edge density |E_(g)|/|V_(g)|. Forexample, increasing k will improve edge density because min(g) isincreased and |V_(g)| is decreased in the same time.

Further, the cohesive subgraph identification device 104 generates (k,d) cores via a Zigzag algorithm (algorithm 1 as depicted in FIG. 6a ) orNodeFirst algorithm (algorithm 2 as depicted in FIG. 6b ). A transientcluster in image stream Q is defined by a (k, d)-Core g(V_(g),E_(g)) inpost network G(V,E), where k>d>0 and;

(1) For every post pεV _(g) ,|N(p)|≦k

(2) For every edge e(p _(i) ,p _(j))εE _(g) ,|N(p _(i))∩N(p_(j))|≦d  (1)

In (k, d)-Cores, the cohesive subgraph identification device 104 uses kto adjust the edge density, and uses d to control the strength ofsimilarity witness. That is, the (k, d)-Cores have an additional factorto control the strength of the similarity between subgraphs generatedthan k-Cores. The cohesive subgraph identification device 104 is thusable to generate a maximal (k, d)-Core which is a subgraph of a maximalk-Core. However, as compared with k-Core, (k, d)-Cores have a morecohesive internal structure enhanced by at least d common neighbors aswitnesses of commonality between the nodes connected by each edge. Thisenhancement makes posts in (k, d)-Core are more likely to tell the samestory.

An example of 3-Core and (3,1)-Core is exemplarily shown in FIG. 5a ; p4is in 3-Core but not in (3,1)-Core, because the similarity between p1and p4 is not witnessed by other posts.

This defines a new kind of cohesive subgraph called k-Dense, in whichevery edge has at least k witnesses. A k-Dense is a (k, k+1)-Core. Thus,k-Dense cannot provide users the flexibility to adjust k and dindependently. Therefore, a (k, d)-Core generated by the cohesivesubgraph identification device 104 is a better definition than k-Coresand k-Dense.

The cohesive subgraph identification device 104 generates (k, d) coresvia a Zigzag algorithm (algorithm 1 as depicted in FIG. 6a ) orNodeFirst algorithm (algorithm 2 as depicted in FIG. 6b ).

Given a network G(V,E), the cohesive subgraph identification device 104recursively removes the nodes with degree less than k from G(V,E), untilall the remaining nodes have a degree at least k. The result is a set ofk-Cores. The k-Cores can be obtained in polynomial time and the k-Coresform the basis for the (k, d)-Cores generation algorithms.

In an exemplary embodiment, the cohesive subgraph identification device104 generates the (k, d)-Cores according to the “Zigzag” algorithm ofFIG. 6a . It should be noted that the algorithm is named “Zigzag”because it is repeatedly changing the property of the current networkbetween two states: the first state is (k, d)-Core set G1 obtained bynodes recursively, and the second state is (d+1, d)-Core (or d-Dense)set G2 obtained by removing edges recursively. The Zigzag process willend if each connected component in the result set is a (k, d)-Core, orthe result set is empty. The Zigzag algorithm takes polynomial time andthe result is exact.

Given a network G(V,E) and numbers k and d, removing n nodes with degreeless than k recursively can be more efficient than removing n edges withless than d witnesses recursively in generating (k, d)-Cores.

That is, deleting an edge needs to check the common neighbors of two endposts, while deleting a node only needs to check its degree. Moreover,since |E|>>|V|, deleting one node will remove many edges at the sametime, making the network shrink fast.

Therefore, in another exemplary embodiment, the cohesive subgraphidentification device 104 generates the (k, d)-Cores according to the“NodeFirst” algorithm as exemplarily shown in FIG. 6 b.

The NodeFirst algorithm can improve on the Zigzag algorithm by applyingnode deletions as much as possible, while not compromising the qualityof the solution. The heuristic is, whenever an edge is deleted, thecohesive subgraph identification device 104 checks whether the degree ofany end node is smaller than k, and if it is, a recursive node deletionprocess will be performed (i.e., as in Line 7 of FIG. 6b ).

The algorithm is named “NodeFirst” figuratively because a single edgedeletion may result in a complete k-Core node deletion process. Sincethe cohesive subgraph identification device 104 does not need to performa complete edge deletion process as in the Zigzag algorithm does in Line3 in FIG. 6a , NodeFirst can make the given network G(V,E) convergequicker to the set of maximal (k, d)-Cores.

Referring to FIG. 5a , in an exemplary embodiment, points p₁, p₂, p₃, p₄and the other three nodes represent images input into the outlier andinlier detecting system 101 in the form of a K-core. The linesconnecting the points show how similar the images are to each other. Theline represents a strength of similarity between each image of theK-core. That is, each point in the K-core has a weight associated withit and the k-Core shows the strength of similarity between each image.The cohesive subgraph identification device 104 uses the Zigzag orNodeFirst algorithm to generate the (k, d) core which includes pointsp₁, p₂, p₃ which further increases the certainty of strength ofsimilarity between each point as shown in the right side of FIG. 5a .That is, the algorithms determined that an edge point (i.e., point p₄)was to be removed and further reduce the k-Core to the (k, d)-core.

Thus, the cohesive subgraph identification device 104 identifiessubgraphs (i.e., (k, d)-Core structures as shown in the right side ofFIG. 5a ) using either one of the algorithms and outputs the identifiedsubgraphs to the subgraph correlation tracking and clustering device105.

For example, as exemplary shown in FIG. 4, if exemplarily 10,000 imagesare input into the system and the algorithms are applied to the 10,000images, the cohesive subgraph identification device 104 identifiesmultiple (k, d) cores (each image) which represent a subgraph output bythe cohesive subgraph identification device 104. The cohesive subgraphidentification device outputs a plurality of subgraphs each having a (k,d) core associated with the subgraph.

The subgraph correlation tracking and clustering device 105 receives theplurality of subgraphs from the cohesive subgraph identification device104, and by correlation and measuring by propagation and aggregationprocess is able to combine different subgraphs together based on asimilarity factor between (k, d) cores of the plurality of subgraphs.

Thus, the subgraph correlation tracking and clustering device 105 can beconfigured to determine a relationship between different subgraphs basedon a similarity factor between the (k, d)-cores and the plurality ofsubgraphs output by the subgraph correlation tracking and clusteringdevice 105. Further, the subgraph correlation tracking and clusteringdevice 105 can be configured to combine the different subgraphs togetherbased on the similarity factor between the (k, d)-cores of the pluralityof subgraphs.

Referring to FIG. 4, in an exemplary embodiment, the left half of theimage can represent one subgraph output by the cohesive subgraphidentification device 104 and the right half can represent a secondsubgraph. Based on the similarity between the (k, d) core of each of thesubgraphs, the subgraph correlation tracking and clustering device 105combines the different subgraphs.

In an exemplary embodiment, assume that the face of interest is person Xand that Person Y and Person Z are also included in the list of imagesinput into the system, the subgraph correlation tracking and clusteringdevice 105 can combine all subgraphs of person X (i.e., inliers) whileremoving all subgraphs containing Person Y and Person Z (i.e.,outliers). That is to say, the detected outliers have no correlation orsimilarity to the image of interest.

More specifically, to calculate whether to combine different subgraphsor whether to remove the subgraphs, the subgraph correlation trackingand clustering device 105 uses a transient subgraph filtering approach.

The subgraph correlation tracking and clustering device 105 uses apropagation and aggregation (P & A) approach to measure correlationsbetween subgraphs. The subgraph correlation tracking and clusteringdevice 105 performs the P & A process along edges to compute thecorrelation between a given cluster and all other clusters.

To start, the subgraph correlation tracking and clustering device 105uses two kinds of nodes in the post network; source nodes and targetnodes. On each iteration, each source node propagates its edgesimilarities to its neighborhood along paths, and meanwhile, each targetnode aggregates the similarities that are propagated in from theirneighbors. The source and target denote the source node set and targetnode set respectively, as shown in FIG. 5 b.

Given the maximal path length l, path similarities aggregated by a nodepj 2 Target is shown in equation (2) where P_(i)(p_(i), p_(j)) is theset of simple paths from p_(i) to p_(j) within maximal 1 hops. Sim(path)is the similarity of a path, which can be computed by multiplying alledge similarities along the path, dampened by factor c (0<c<1) on eachhop.

PA(Source,p _(j))=ΣΣsim(Path)  (2)

For example, supposing path=(p_(i),p_(i+1), . . . , p_(i)+x,p_(j))(X<L),the result is equation (3) shown below:

Sim(path)=c ^(x+1) S(p _(i) +x,p _(j))  (3)

Based on the above equation (3), the subgraph correlation tracking andclustering device 105 applies P & A process to the computation of storycorrelations. The subgraph correlation tracking and clustering device105 measures the correlation between SStory_(i)=g_(i)(V_(i),E_(i)) andStory_(i)=g_(i)(V_(j),E_(j)) as the amount of average path similaritiesthat are propagated and aggregated between them. Since post network isundirected, PA(V_(i), V_(j))=PA(V_(i), V_(j)) where Source=V_(i),Target=V_(j), and path similarities that are propagated from V_(i) andaggregated at nodes in V_(j).

Thus, the subgraph correlation tracking and clustering device 105measures correlations between subgraphs as Corr(gi, gj) using P & Awhich are computed by equation (4):

Corr(g _(i) ,g _(j))=PA(V _(i) ,V _(j))=PA(V _(i) ,V _(j))  (4)

Referring now to FIG. 6c , the subgraph correlation tracking andclustering device 105 uses algorithm 3 and the equations above to detectoutliers within the input images as shown in FIG. 3. Based on thecorrelation between the subgraphs and similarities, the subgraphcorrelation tracking and clustering device 105 uses algorithm 3 of FIG.6c and combines subgraphs to output inliers as shown in FIG. 4.

It should be noted that although images of faces were uses for theexemplary embodiments described herein, any image of an object or entitycan be input into the similarity detection system 101.

FIG. 2 shows a high level flow chart for a similarity detecting method200 for detecting similarities.

Step 201 inputs entities into the similarity method 200.

Step 202 performs face detection and verification by extracting a faceregion from the original image and extracting SHIFT feature from theface region.

Step 203 performs multi-scale SIFT and uses spatial histograms asdescriptors and generates a similarity matrix between the input entitiesbased on attributes of the input entities.

Step 204 identifies cohesive subgraphs from the input entities.

Step 204 a generates k-cores from the similarity matrix.

Step 204 b further generates (k, d) cores via a Zigzag algorithm(algorithm 1 as depicted in FIG. 6a ) or NodeFirst algorithm (algorithm2 as depicted in FIG. 6b ).

Step 204 c identifies subgraphs from the (k, d) core structure.

Step 205 uses a propagation and aggregation (P & A) approach to measurecorrelations between subgraphs. Step 205 determines a relationshipbetween different subgraphs based on a similarity factor between thesecond parameter and the plurality of subgraphs

Step 205 a performs the P & A process along edges to compute thecorrelation between a given cluster and all other clusters.

Step 205 b combines different subgraphs together based on a similarityfactor between (k, d) cores of the plurality of subgraphs.

Step 206 outputs outliers and inliers of the input entities.

Exemplary Hardware Aspects, Using a Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 7, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 7, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 8, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 8) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the similarity detection system 101 described herein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A similarity detection system receiving aplurality of input entities, the system comprising: a face detection andverification device configured to extract a face region from theplurality of input entities and to extract shift features from the faceregion as images, the face detection and verification device excludesitems in the input entities that have facial features but are not a realface of a person; a cohesive subgraph identification device configuredto calculate, based on attributes of the plurality of input entities, afirst parameter and a second parameter based on the first parameter, andfurther configured to identify a plurality of subgraphs from the secondparameter; and a subgraph correlation tracking and clustering deviceconfigured to determine a relationship between different subgraphs basedon a similarity factor between the second parameter and the plurality ofsubgraphs, wherein the subgraph correlation tracking and clusteringdevice is further configured to output outliers that include thesubgraphs not combined by the subgraph correlation tracking andclustering device, wherein the subgraph correlation tracking andclustering device uses source nodes and target nodes of nodes in a postnetwork such that on each iteration, each source node propagates itsedge similarities to its neighborhood along paths, and, each target nodeaggregates the similarities that are propagated in from their neighbors,and wherein the subgraph correlation tracking and clustering devicemeasures the correlations between subgraphs to detect outliers withinthe images and based on the correlation between the subgraphs andsimilarities, the subgraph correlation tracking and clustering devicecombines subgraphs to output inliers.
 2. The similarity detection systemaccording to claim 1, wherein the plurality of input entities comprise alist of images.
 3. The similarity detection system according to claim 1,wherein the subgraph correlation tracking and clustering device isfurther configured to output inliers that include the subgraphs combinedby the subgraph correlation tracking and clustering device.
 4. Thesimilarity detection system according to claim 1, wherein the attributescomprise a mathematical representation of the entities.
 5. Thesimilarity detection system according to claim 1, wherein the secondparameter includes an additional factor to control a strength of thesimilarity factor between entities than the first parameter.
 6. Thesimilarity detection system according to claim 1, wherein the cohesivesubgraph identification device calculates the second parameter usingeither a Zigzag algorithm or a NodeFirst algorithm.
 7. The similaritydetection system according to claim 1, wherein the first parameterincludes k-cores and the second parameter includes (k, d)-cores, whereinthe (k, d)-cores include a cohesive internal structure enhanced by atleast d common neighbors as witnesses of commonality between nodesconnected by each edge than the k-cores, and wherein d is an integergreater than zero.
 8. A similarity detection method, comprising:receiving a plurality of input entities; extracting a face region fromthe plurality of input entities and shift features from the face regionas images; excluding items in the input entities that have facialfeatures but are not a real face of a person; calculating, based onattributes of the plurality of input entities, a first parameter and asecond parameter based on the first parameter; identifying a pluralityof subgraphs from the second parameter; determining a relationshipbetween different subgraphs based on a similarity factor between thesecond parameter and the plurality of subgraphs; outputting outliersthat include the subgraphs not combined by the combining; using sourcenodes and target nodes of nodes in a post network such that on eachiteration, each source node propagates its edge similarities to itsneighborhood along paths, and each target node aggregates thesimilarities that are propagated in from their neighbors; and measuringthe correlations between subgraphs to detect outliers within the imagesand based on the correlation between the subgraphs and similarities, andcombining subgraphs to output inliers.
 9. The similarity detectionmethod according to claim 8, wherein the plurality of input entitiescomprise a list of images.
 10. A non-transitory computer-readablerecording medium recording a similarity detection program, the programcausing a computer to perform: receiving a plurality of input entities;extracting a face region from the plurality of input entities and shiftfeatures from the face region as images; excluding items in the inputentities that have facial features but are not a real face of a person;calculating, based on attributes of the plurality of input entities, afirst parameter and a second parameter based on the first parameter;identifying a plurality of subgraphs from the second parameter;determining a relationship between different subgraphs based on asimilarity factor between the second parameter and the plurality ofsubgraphs; outputting outliers that include the subgraphs not combinedby the combining; using source nodes and target nodes of nodes in a postnetwork such that on each iteration, each source node propagates itsedge similarities to its neighborhood along paths, and each target nodeaggregates the similarities that are propagated in from their neighbors;and measuring the correlations between subgraphs to detect outlierswithin the images and based on the correlation between the subgraphs andsimilarities, and combining subgraphs to output inliers.
 11. Thenon-transitory computer-readable recording medium according to claim 10,wherein the plurality of input entities comprise a list of images. 12.The similarity detection system according to claim 1, wherein the facedetection and verification device identifies the face region from theplurality of input entities that is in fact the face and verifies thatthe region is actually the face of the person.
 13. The similaritydetection system according to claim 1, wherein the face detection andverification device verifies that only one face exists in the images ofthe plurality of input entities.
 14. The similarity detection systemaccording to claim 13, wherein the face detection and verificationdevice identifies a number of face regions detected in the images andremoves images from the input entities with multiple faces in theimages.